SVM parameters tuning with quantum particles swarm optimization

Common used parameters selection method for support vector machines (SVM) is cross-validation, which is complicated calculation and takes a very long time. In this paper, a novel regularization parameter and kernel parameter tuning approach of SVM is presented based on quantum particle swarm optimization algorithm (QPSO). QPSO is a particle swarm optimization (PSO) with quantum individual that has better global search capacity. The parameters of least squares support vector machines (LS-SVM) can be adjusted using QPSO. Classification and function estimation are studied using LS-SVM with wavelet kernel and Gaussian kernel. The simulation results show that the proposed approach can effectively tune the parameters of LS-SVM, and improved LS-SVM with wavelet kernel can provide better precision.